Monday, November 27 | 3:10 p.m.-3:20 p.m. | SSE06-02 | Room N228
In this presentation, Italian researchers will share how mobile CT scanners can play a pivotal role in the personal identification of cadavers in mass disasters.On April 2015, a fatal shipwreck occurred in the Mediterranean Sea, resulting in the tragic loss of approximately 700 migrants.
"Whenever mass disasters occur, the sheer number of bodies makes it impossible to arrange viewing for all of the bodies or to store the bodies for later identification," Dr. Federica Vernuccio told AuntMinnie.com. "A clear advantage of CT was the possibility of storing data for further evaluations."
In the case of cadavers recovered from water, the loss of key facial and bodily features necessitates technological imaging to identify them, she explained.
In July 2016, researchers from the University of Palermo used a mobile CT scanner to perform scans of the 149 cadavers (still inside their body bags) that were recovered. CT scanning helped to quickly identify the patients' age, sex, stature, dental profile, previous pathological conditions, and even personal belongings.
"Although a routine radiological assessment in migrant shipwrecks is almost impossible, our study allowed us to get an overall idea of the living conditions of the migrants during these desperate journeys," Vernuccio said. "The protocol we created can be potentially used in other types of mass disaster."


















![Images show the pectoralis muscles of a healthy male individual who never smoked (age, 66 years; height, 178 cm; body mass index [BMI, calculated as weight in kilograms divided by height in meters squared], 28.4; number of cigarette pack-years, 0; forced expiratory volume in 1 second [FEV1], 97.6% predicted; FEV1: forced vital capacity [FVC] ratio, 0.71; pectoralis muscle area [PMA], 59.4 cm2; pectoralis muscle volume [PMV], 764 cm3) and a male individual with a smoking history and chronic obstructive pulmonary disorder (COPD) (age, 66 years; height, 178 cm; BMI, 27.5; number of cigarette pack-years, 43.2, FEV1, 48% predicted; FEV1:FVC, 0.56; PMA, 35 cm2; PMV, 480.8 cm3) from the Canadian Cohort Obstructive Lung Disease (i.e., CanCOLD) study. The CT image is shown in the axial plane. The PMV is automatically extracted using the developed deep learning model and overlayed onto the lungs for visual clarity.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/03/genkin.25LqljVF0y.jpg?auto=format%2Ccompress&crop=focalpoint&fit=crop&h=112&q=70&w=112)

